In the area of financial payments, there is increasingly a consolidation of payment methods. Historically, a credit or debit card is linked to a single funding account. Analytic models have typically been developed with this “one card, one account” arrangement, either implicitly or explicitly. Today, financial institutions such as banks are introducing new payment devices in the form of plastic cards or electronic and wireless communication mobile devices that can be linked to one or more financial accounts such as, credit, debit, savings, points/mileage accounts, etc. However, traditional analytic systems such as fraud detection are still designed and optimized for single-account cards.
Funding accounts can be dynamically chosen for individual payments with such devices. For example, applications that run on mobile devices enable the devices to initiate payments through one or more funding accounts. This shift from one-card-one-account to one-card-multi-account poses considerable challenges to current analytic approaches and also provides an interesting new analytical opportunity to improve the protection for these payment instruments and subsequently the one or more associated funding accounts from fraudulent activity. In the card example, a multi-funding payment instrument can effectively be viewed as several cards in a wallet linked to different accounts. Traditional payment fraud detection methods may not be well-suited to monitor fraud as they assume a single linked account and generally do not reflect the selection of funding account and patterns across funding accounts.